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    Please use this identifier to cite or link to this item: http://tkuir.lib.tku.edu.tw:8080/dspace/handle/987654321/108941

    Title: Rough Classifier Based on Region Growth Algorithm for Identifying Liver CT Image
    Authors: Cheng, Ching-Hsue;Wei, Liang-Ying
    Keywords: Computer-aided Detection;Liver Tumor;Abdominal CT Image;Wavelet Packet Transform;Rough Set Theory
    Date: 2016-03
    Issue Date: 2016-12-20 09:40:45 (UTC+8)
    Publisher: 淡江大學出版中心
    Abstract: Over decades, liver cancer is a rising cause of death in Taiwan, and more and more researchers
    are concerned about detecting hepatic tumors in computed tomography (CT) images. For clinical
    applications in terms of diagnosis and treatment planning, image segmentation on abdominal CT is
    indispensable. Patients with a large number of CT images need specialist physicians to identify, and
    detecting tumor location correctly from many CT images has been a major challenge subsequently.
    Therefore, this paper proposed a novel computer-aided detection (CAD) method that had high
    classification accuracy for identifying tumors. The proposed method used a region growing algorithm
    to segment liver CT images, employed REDUCT sets to reduce attributes, and then utilized a rough set
    algorithm to enhance classification performance. To evaluate the classification performances, the
    proposed method was compared with five different classification methods: decision tree (C4.5 and
    REP (reduced error pruning)), multilayer perceptron, Naïve Bayes, and support vector machine
    (SVM). The results indicate that the proposed method is superior to the listing methods in terms of
    classification accuracy.
    Relation: Journal of Applied Science and Engineering 19(1), pp.65-74
    DOI: 10.6180/jase.2016.19.1.08
    Appears in Collections:[淡江理工學刊] 第19卷第1期

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